High Memory Overhead Research Articles

Incremental Volume Rendering Using Hierarchical Compression

AbstractWe present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear‐Warp Factorisation method which does not require specialised graphics hardware. However the original Shear‐Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear‐Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. Results from tests using typical volume datasets will be presented which demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements.

The effectiveness of affinity-based scheduling in multiprocessor network protocol processing (extended version)

Techniques for avoiding the high memory overheads found on many modern shared-memory multiprocessors are of increasing importance in the development of high-performance multiprocessor protocol implementations. One such technique is processor-cache affinity scheduling, which can significantly lower packet latency and substantially increase protocol processing throughput. We evaluate several aspects of the effectiveness of affinity-based scheduling in multiprocessor network protocol processing, under packet-level and connection-level parallelization approaches. Specifically, we evaluate the performance of the scheduling technique (1) when a large number of streams are concurrently supported, (2) when processing includes copying of uncached packet data, (3) as applied to send-side protocol processing, and (4) in the presence of stream burstiness and source locality, two well-known properties of network traffic. We find that affinity-based scheduling performs well under these conditions, emphasizing its robustness and general effectiveness in multiprocessor network processing. In addition, we explore a technique which improves the caching behavior and available packet-level concurrency under connection-level parallelism, and find performance improves dramatically.

An overview of the A architecture for optimisation problems in a logic programming environment

Search of a state space is a common function in artificial intelligence applications consuming a large proportion of the total processing time. In this paper we propose a hierarchical architecture using content addressable memory to accelerate the A* algorithm in a Prolog system and remove the associated high memory overheads.